Abstract Biological networks such as protein networks provide an integrated perspective on how proteins work together and are becoming important tools to study neuropsychiatric disorders such as schizophrenia. Mass spectrometry (MS) based proteomics are rapidly advancing and are now capable of quantifying proteins with increased sensitivity and throughput, which provide critical data sources for protein networks and have been emerging as important application in the study of psychiatric diseases. For example, in our recent study, the synaptic protein co-expression network was found to be altered in the auditory cortex of schizophrenia patients. Whereas a variety of network analysis methods have now been developed for microarray data, methodologies customized to proteomic data are lagging far behind. In addition, these methods mainly focus on pairwise marginal correlations while ignoring the joint effects from other genes when constructing the network, failing to distinguish causal interactions from correlations via intermediate genes. Moreover, most existing methods for network testing are permutation based, from which the p-values could be invalid if the permutation-based null distribution is inaccurate. The probabilistic graphical model based differential network inference is more desirable as it infers conditional dependency by adjusting for the joint effects from all other proteins and guarantees to be valid and powerful when the distributional assumptions are satisfied. The objective of our proposed research is to develop, validate and apply novel and robust statistical methods to construct, analyze and infer protein networks from two popular proteomic platforms, namely, the targeted- MS and the unbiased differential-MS. The novel methodology will be immediately applied to the ongoing schizophrenia projects at the University of Pittsburgh, to facilitate novel analyses to identify protein alterations contributing to the disease pathology. First, we will develop novel network construction methodology based on a partial-correlation-based approach, which is under the Gaussian Graphical Model (GGM) framework and quantifies the correlation between two proteins after excluding the effects of other proteins, for protein network construction. Then, we will develop a novel differential network inference procedure, based on the recent development of GGM theory and associated inference, to formally test network differences. Finally, we will thoroughly validate the proposed methods using both statistically simulated data and the real data from a biological model with well characterized network interactions. Robustness of the networks will be assessed using rigorously designed replicate experiments with samples from post-mortem brain tissues of normal subjects. In summary, the novel methods and findings from this research will provide critical guidance for the design, analysis and validation of ongoing and future network studies that utilize proteomics approaches in psychiatric disorders, which will greatly improve the sensitivity and validity of the consequent scientific findings.